Round Up September 2026

0:17Jim Miller: Hi everyone and welcome to this month's Recruiting Future Roundup Live. Brilliant to be live again and talking to everyone today. If you've not watched Roundup before, it's a summary of some of the best episodes that have appeared on the Recruiting Future podcast over the last month or so. And once more, I'm delighted to be joined by an excellent co-host, Jim. Welcome to Roundup. Thank you very much. Thanks for having me, Matt. A pleasure. Could we sort of kick off with you just introducing yourself and telling everyone a bit about what you do?

0:50Matt Alder: Yeah, I'm Jim Miller. I lead the people and talent function for Ashby. Ashby's an ATS, I'm pretty certain most of the audience will have heard of us. I live in the US, despite the accent. I've been in America for nearly 11 years now. I moved over with Google. I spent 15 years with them. And I've been at Ashby for the last three and a half years.

1:10Jim Miller: Fantastic. So we are looking at four episodes that have been on the podcast in the last month. It's really interesting, actually, because we've sort of covered a few sort of diverse subjects over the last few weeks. And I think there are some interesting, definitely some interesting things to talk about here. Looking forward to the discussion. So we'll start off with episode 823, which is called How AI is Disrupting Entry-Level Jobs. And it's an interview with Ruben Obana, who is the co-founder of the Marcy Lab School. Now, this interview came about, I was talking at the ERE conference earlier in the year, and Marcy Lab School were there as an exhibitor.

1:52Jim Miller: And I had a really interesting conversation with them and we thought it would be great to do a podcast. So what they do is they're a sort of an alternative education institution for people to teach them tech skills, particularly sort of AI skills and help them get that first job in tech. So really kind of set up as an alternative to going to college. In the interview, we were talking about how entry-level hiring and entry-level work is really changing in the tech sector. Jim, what did you think of this one? I know it's something close to your heart, as it were. It is.

2:31Matt Alder: And it was really interesting because obviously I look a lot at what's going on in the world of AI, but it felt really analogous to me from about 20 years ago. So I joined Google as a tech recruiter to build out the office there in London. And there weren't any engineers at this point. And what I realized while watching our recruiting programs teams is they were doing exactly this kind of work. They were going into universities to teach the professors what technologies we were using because those professors wanted all of their students to come and work at Google. and it was interesting that we were in effect doing exactly what Ruben was describing there and what I found back then is that it was the students who took control and students were going out and doing their own open source coding building their own projects doing their own hobbyist work publishing this stuff and that was what we were using to determine whether the person was strong enough in this space it was in effect giving them the professional experience in coding you know getting code reviews and everything else on on what they were their production um and it was fantastic way of them showcasing their skills so they're kind of circumnavigating what the education system was forcing on them um and i think that's probably what's going to happen now that the the younger folks are going to be picking up these ai skills they're going to be building their own portfolios of technology that they've produced um showcasing apps or whatever else it happens to be.

4:01Matt Alder: And that's going to be their leverage point. So the starting point that institutions like Rubens are giving them to get them up and running, and then enabling them to do this work, I think will avoid the space that we're going to see in the entry level jobs market.

4:15Jim Miller: Yeah, I think that's really interesting. And what kind of struck me was the speed at which they could adapt their curriculum and the way that they work to sort of keep up with the market. Yeah. On the part about entry-level jobs changing, there's a few things that we sort of talked about here. The feeling was that the entry-level work wasn't disappearing, but the bar was getting higher. New starters were being asked to do things that mid-level people might do in the past. Is that something that you're seeing? Is the nature of those jobs actually changing with

4:54Matt Alder: the technology yes and no there's still the very very highest echelon of students who are being taught by the people who taught the real trailblazers in the ai space yeah those folks are being picked up for entry-level roles yeah it's a build versus buy kind of scenario they could wait two years and then have to spend millions and millions of dollars to hire these individuals so that's still happening um on the other side it's the work that i'm seeing i'm seeing engineers move back to writing the code themselves and using the ai as more assistive technology for the code reviews testing and testing the the way that they've solved the problem um research on what the best solution might be and i think we've seen with certain uh companies having to lay off people because they've overspent on AI tokens, that the cost of the AI doing all of the work actually outstrips the cost of the human doing some of this.

6:01Matt Alder: And it's natural economics. I think that that's the swing that we're seeing, that we'll start to see more humans being brought into the space so that it's not all being done by AI because someone's got to pay the bill for all of these data centers that's being built, right?

6:14Jim Miller: Yeah, I think that's interesting. It's something we're actually going to cover a little bit deeper when we talk about the next episode. Before we sort of move on, there was something else that sort of really stood out for me here that is a kind of a recurrent theme. So Ruben was talking about the skills that people have to learn and actually, you know, judgment, collaboration and communication have become even more important in this kind of post-AI world that we live in. How can people learn that? how can people get that is it something that they can be taught in the education system is it something they have to learn on the job has has ai shifted the dynamics here what's your sort of view on that on that judgment piece i i don't think so and i'll touch on this when we talk

7:02Matt Alder: about the next episode as well we're an asynchronously remote company and we still find ways for individuals to meet, interact virtually or in person. So that's key. But I think it comes all the way back into the opportunities that you have for team sports or any other element. Whatever you can do to throw yourself into social situations is going to help your development naturally so that when you then get to this world of work, that the collaboration between the humans is going to remain critical, in my opinion, that the creativity piece of two humans coming together and creating something that wouldn't have been produced by either of them is still the powerful point.

7:50Jim Miller: Yeah, I couldn't agree with you more on that one. It's probably a good point to leave this one and move on to the next episode, which was actually the next episode of the podcast. So episode 824. um it's interview with matt campbell who is one of the managing directors at alvarez and marcel and interestingly it's called is ai creating talent debt and we covered a few topics in this podcast but the central piece to that title was what you were saying about uh big tech companies laying lots of people off because ai is changing everything in the way that they work and then having to hire those people back again or rethink it.

8:32Jim Miller: And, you know, lots of companies kind of copying that model and being burnt by it. And that's the talent debt part to it. Talk us through that a little bit.

8:42Matt Alder: I think some of it's mislabeling. I think there was a big reset of wages based on the wage inflation we saw of 2021 and early 2022. And the company's just looking going, well, we can cut these folks and rehire in effect the same positions to reset there and very much using AI as the excuse. But I think that the underlying piece that we just touched on of the collaboration, there was a reason why Google had long lunch tables. And it was to throw people together, to have conversations, people who wouldn't know each other normally, wouldn't have a chat normally, bringing two different mindsets, two different sets of experience together to create something new.

9:22Matt Alder: And some of, certainly in the early days, some of Google's biggest products came out of that kind of interaction over a game of pool in the office, a game of foosball over the long lunch table. And you could see an app donut, for example, in Slack that brings groups of employees together on a random basis. I have two of them here today later on. And I love those moments where you can get together. So I think that's the big pain point because when you strip out huge swathes of an organization in the guise of a layoff. You're not necessarily picking who's going. It's just the job disappears rather than the humans.

10:03Matt Alder: And you are going to lose so much there. Those gaps will take a while to show themselves, by which point those people have gone off and had new opportunities and probably driven careers that would make them not want to go back into that organization. so it it's it's an odd state of affairs but i think companies are going to learn from it very quickly but i think what it will do is it'll tighten up how they hire in the first place and make them much more intentional to get the best out of the folks that they do bring in

10:33Jim Miller: yeah i mean it came out sort of really clearly in the conversation because we were talking about a job being a bundle of tasks but actually a role was the role that someone plays in the organization's social system and that was a big part of it and that's something that um these kind of paper exercises and things just can't you know can't can't can't see and it's just um it's kind of such a critical such a critical part to this the the other part of this conversation was really around the commercial value of ai and how people are implementing it because that's something that you know matt kind of works on the strategy for um there's two bits this really the first one was around ai pilots and they mostly end inconclusively because people are using them just to test whether the technology works not whether it actually solves a business problem what what's your kind of view on part you know obviously you work for a software company as well you know what's your view on on pilots that that you do or pilots that you see your customers do are people getting it

11:34Matt Alder: right um no they're not because the hypothesis is the important thing when you're doing something like this and the hypothesis is generally that we're going to save money what they don't do is they don't intentionally repurpose the time that will be saved by the technology and matt touches on it um sorry he touched on it a lot during the the podcast um the analogy i use is is combine harvesters. Okay. I grew up in the middle of Suffolk. The farmer buys a combine harvester for the first time and he's got this labor force that used to do the harvest. If he lets them sit on the side of the field drinking cider, he's got zero ROI from the deployment of that technology in the three seconds that the combine takes to do a day's work for an entire team of humans.

12:23Matt Alder: That's the point. You have to isolate. So if you put those workers to work, building a mill to mill the grain so you go deeper into the supply chain or diversify your business and growing other crops that are more labor intensive, you've got an ROI. And it's Parkinson's law of productivity. Work fills the time available to you. So if the human is given two hours back per week, they're going to do tasks that previously weren't a priority. Ergo, you've got no ROI. You flip it around the other way. If you've got 20 workers and they're each saving two hours per week, if you can isolate one person and take them out of that group, that's the 40 hour work week.

13:04Matt Alder: You repurpose that person somewhere else, that pilot enables that 40 hours to be saved. And now you have a force multiplier going elsewhere. That's true ROI. So it's how you frame the pilot and how you isolate the expected time saved is the only way to prove or disprove your hypothesis.

13:25Jim Miller: It's so important because I made a list of these kind of phrases that I get back from people when I said, you know, if AI is going to save all this time for your team, what are they going to do that's different? And you tend to get very, very generic things like they're going to do the important work or they're going to do what they always should have been doing. And when you dig a bit deeper, there's just nothing kind of behind there. So it's so critical because I think so many, I mean, you know, specifically in TA, you know, really thinking about, well, where do we add value to the business?

13:56Jim Miller: How can we redeploy people? And really, as you say, get the ROI from this.

14:00Matt Alder: For me, I mean, we've got a whole suite of AI functionality and each piece is working in series together to give a compound impact on the productivity of our team members. It means I probably have a smaller team than I would have done for a company growing at our pace five years ago. It means that I can reinvest that time in quality and quality, our operating principle number five is we hire frustratingly patiently or we're frustratingly patient about hiring, which to me is a mandate for quality. So I'm able to refocus what our team does and how we hire. So for example, we build cohorts of candidates before anyone gets put into an interview.

14:39Matt Alder: the very equivalent to the UK kind of civil service model, but I've deployed it in a B2B SaaS company to try and create a shortlist at the end of the process of multiple candidates who are above the bar to create choice, choice driving the highest quality. I'm able to do that because we can spend the time building the pool at the beginning and then work incredibly quickly using the assistive technology and the tool to get to that stage. Ergo, we end up with stronger results than what we were expecting from the workforce that we hire, the two sides of talent density. We're not trying to hire the smallest workforce, we're trying to hire the workforce we plan for and then get a better than expected results.

15:19Matt Alder: That's the outcome from our deployment of AI technology earlier on in the recruitment process. We've thought deeply and intentionally about that.

15:28Jim Miller: Yeah, and what I like about that is that you have basically used the technology to have a completely different process to give you those great results rather than using the technology to make your old process quicker. I think that's such a great example of what's possible, a million percent. Let's move on to the next one. Slight change of topic, but something equally important. So episode 825, the real risks of candidate fraud. Now, what's interesting, So interview with Lauren Fury is the principal product manager at Proof who make technology to help with to help protect people from candidate fraud.

16:09Jim Miller: The first thing to say about this is that up till a few weeks ago, I think that a lot of people just kind of lumped a lot of things together. So fraud, cheating, using AI in the recruitment process all seem to end up in the same bucket. And what I think is really important about this conversation is it very much pulls out the fraudulent, criminal, dangerous activity that we are seeing in the recruitment process. And I think if you, again, a few months ago, people would have thought of this as something that wasn't affecting many companies, as something that was a bit of a one-off. But I've seen the volume turn up on this discussion.

16:48Jim Miller: So what we mean by fraud here is basically criminal and state actors targeting companies to try and get jobs to get inside their systems for whatever purposes. So, you know, organized criminal gangs, North North Korean government. It's kind of happening. And more and more I'm finding companies who have kind of had this problem. Obviously, this is kind of, you know, core in terms of what you do as well. How big a problem is this? And are people taking it seriously enough?

17:20Matt Alder: It's huge and they are. Just for ourselves, we did an analysis last quarter and I think I was at 16 % of applications for software engineering roles we deemed fraudulent using our own system. Wow. Which has a different way of working to proofs. Yeah. But it was flagging 16 % of them as potential to high potential fraud. and specifically around the person not being the person they said they were. So again, I'm using that tight definition that you've already just used. Yeah, exactly.

17:58Jim Miller: And I think the other thing that sort of struck me about this is how ill-equipped a lot of TA teams are to spot this. So you see lots of people sort of talking about it. How do we avoid it? Lots of kind of almost like home-baked remedies about, well, actually get someone to put their hand in front of their face or do all this kind of stuff. But these are quite sophisticated attacks and you need the kind of proper tools to combat them, don't you?

18:22Matt Alder: You do. Am I able to tell you a little bit about how Ashby does it?

18:27Jim Miller: Yeah, of course.

18:27Matt Alder: I never like to be selling. Proof's model is very much proving who the person is through traditional ID verification. Ashby's model, which has been live for well over a year now, is an analysis of all of the inputs coming through, all of the signal inputs you get when you apply. And it takes every single element line by line. There's no stack ranking or anything else there to identify potential issues. I won't go on to the nuance of it because I don't want people to then start to re-engineer or try to attack it by the information I'm giving. But when the person is reviewing the resume, all of these signals are displayed to the user.

19:10Matt Alder: So they can then make their decision. And a lot of the conversation here was around how IT departments are in effect leaving the recruiter to take the action themselves. This gives the recruiter all of the information that the IT department would have themselves. And then you can build your own risk tolerance rubric almost as to who you let through the system and so on. So it's an early intervention to give signal. and then if you do want to take it further you can then go on and do a secondary id check after the fact and i think that's the really interesting piece here you've got some solutions that are id verification first yeah creates a barrier to entry which is potentially in conflict with the with the whole concept of recruiting which is to hire someone downstream because if the person chooses not to apply who is great you've got a missed opportunity so that's the needle that we we're trying to thread with this non-invasive initial approach to give the signal directly to the user and then they can follow up with the validation but they've already got the candidate in hand and start building the relationship.

20:15Jim Miller: Yeah I think that makes a lot of that makes a lot of sense and there's obviously a balance here between candidate experience and you know protecting the business and also you know no one likes to be accused of being a fraud if they're not actually a fraud if that makes sense it's not a very good thing to do so there's obviously going to be a you know a debate here balance here different ways of working all of those all of those kind of things but it's obviously really important that TA has tools available to to you know to to do this and also presumably really important that they're working with other areas of the other areas of the business as well so there is a there's a kind of a joined up approach to to this does TA sort of just get forgotten about and left out or do you think companies are identifying that as a route into that kind of security.

21:02Matt Alder: Yeah, absolutely. I mean, we have a hotline to security. We can flag anything at any time. But it's interesting this is happening across functions. I'm hearing about people's products becoming a vector through the sales process to get access to the product, to use the product in nefarious ways, you know, across the B2B SaaS landscape. So there are attacks coming into pretty much every department, which is elevating security as a function. but it's elevating the conduits into each of those departments to truly understand what their processes are to then nip all of this in the bud. That, to me, is the really interesting scale element because the ID fraud in employment is not new.

21:45Matt Alder: No, not at all.

21:46Jim Miller: We saw it with Human to Human

21:48Matt Alder: where you had employees who were selling their own services to do interviews because they knew how to pass them and then somebody else was coming on board. or even the case of twins where one person does the interview and the other person comes to do the job it was wow yeah fascinating stuff and this was still when you were you were human to human in

22:08Jim Miller: an office yeah exactly so it just kind of shows how how can it easily easily it is to is to do in some respects and i think also when we were talking about the future here that this is only going to get worse as um ai kind of really accelerates the way that people attack companies absolutely

22:24Matt Alder: I was chatting to somebody the other day on a trip to London, and he was in security, and he was talking about their patch cadence and about how they used to patch every year, and now how they're already down to patching every day. Wow. It's like a two-year time span, and I don't know anything about this stuff, so I couldn't comment on the technical elements. But to me, that was just like, wow, what a magnitude of change being caused by the capabilities of the nefarious actors. but he was telling me it was actually the good actors who were flagging the system problems so that they could then fix them faster so it's two sides no absolutely i think i think the overriding

23:02Jim Miller: message from from this is i think people should listen to this and you know if you are leading and tape functioning you do not have tools of of some kind to combat this then you need to you need to look at that urgently because um you know this is a tax on an industrial scale to lots of businesses.

23:20Matt Alder: There's also the labor cost because you've still got to process every single one of those applications, for example. If you're spending 25 % more time this year than you were last year on application review, that's a problem that you've got to solve.

23:34Jim Miller: Yeah, absolutely. It's going to become an even bigger discussion point as we move forward, I'm sure of that. So let's move on to the final episode that we're going to talk about, which is 827 agent to agent recruiting. Interview with Matt Wilson, who is the co-founder and CEO of Jack and Jill. And I was actually kind of recorded this in their office. And I think it was the day after they just kind of done a big raise. So they are, you know, a really interesting organization. Now, I want to talk about this in a kind of broader sense, because the agent to agent recruiting, and I'll kind of explain what I mean by that in a second, is something that I've I think is just, it is the future.

24:19Jim Miller: So I've recently got back from RecFest USA, where I did a presentation about agent to agent recruiting and, you know, how I think it's developing. So really the idea here is that the candidate has an AI agent that helps them look for jobs, and the employer has an AI agent that helps them look for candidates. And it's a very simple way of describing it. I think it's the direction everything's moving in. What Jack and Jill have done, they've almost built like a closed system of this. So it's almost like the next generation of job board, if you like. They have one agent that talks to candidates. So everyone who registers gets a 20 minute call conversation with AI to get some more information.

25:04Jim Miller: And they have another agent that then helps employers find people. So it's kind of fascinating. And I think it's a microcosm of where the industry is going. What do you think?

25:15Matt Alder: Funny enough, I actually met Matt two weeks ago in London on camera, and he was interviewing me for their website. So it's kind of full circle. And far too many Matt's this week as well. I think this is our third meeting. I think it's really interesting because I like to think, going back to basics, it's hard enough to get a job, let alone a job that you're not qualified for yeah and that to me is the crux of the situation here where i'm seeing the ai influence on the candidate side i'm seeing the creation of collateral that supports that individual for a job where that collateral isn't grounded in truth yeah and and that's a real problem for us because in order to determine whether that person has that capability we need either need very advanced ai to do it on the employer side or a human and the joke even as long ago as two years was you post a job and one minute later you've got 5 000 applications where your system says they're all qualified yeah exactly we're gonna need more humans yeah right that that's the fun i think there's a third element to this agent to agent piece in that you've got the input from the candidate side, you've got the input from the matching engine agent.

26:44Matt Alder: Then I think there's a third piece, which is agnostic, which is the judgment part. And that's going to be the really interesting piece. So you've got AI interviewers who are pre-programmed to do the interview that a human would do to avoid scale issues and so on, which is fantastic and they're able to dive deep and to give objective feedback back about that individual that's the third piece that i think will have to come into play here and i don't think that the employer's first agent will be combined with the second agent i think they will deliberately separate those two out to sort the wheat from the chaff to use another agricultural analogy um to to drive then the recruitment process from that point i think that's the change that we'll have to see um on the other side the democratization of jobs the ability for to of people to find new jobs to be out there in the marketplace and to give them the opportunities that can only be a positive thing um we've seen it with the remote work revolution and i think that this discovery piece and matching that's just fantastic

27:58Jim Miller: yeah it's um i think there's a few things a few things around this i think that the one of the things that that i've noticed is a lot of there's obviously candidates have access to a lot of tools now many of which are helping to drive this this this kind of noise and signal problem that we that we that we that we have so it's almost like i think i described it as um looking for a needle in a haystack that is a haystack made completely out of needles. So it's kind of interesting. So I think that from a TA perspective, a lot of people are looking at this the kind of the wrong way. I think they're looking at this candidate technology as this is just people grifting, exploiting candidates, it's spam.

28:39Jim Miller: But in and amongst all of that, there's some really interesting companies building candidate technology. And I think a lot of the innovation in the sector is going to come from that because at the moment it is you know a lot of companies are adopting ai processes because they're trying to deal with the things that the candidates are causing so i think that's interesting and i think that anything as you say anything that gives the ability for people to find more opportunities and also to to potentially look at opportunities that they wouldn't have considered but they're actually really qualified for is is kind of fantastic And then on the employer side, I agree with you.

29:14Jim Miller: I think that what this does is it kind of basically enhances those human conversations. It makes them happen quicker. Hopefully, it avoids the situations where people sit down and say, tell me about your resume, talk me through your last few jobs and all that sort of stuff. So, I think it's a real positive thing here. I also think that it's something that TA needs to be prepared for because there are so many ways that approaches like this could go horribly wrong as well in terms of, you know, who owns the data? Who owns the persuasion? You know, I was reading something about, you know, personal AI agents yesterday that someone had written about Facebook's Muse, which was frankly terrifying when I, about psychological manipulation and things like that.

29:55Jim Miller: So, this is a, there's a, there's a lot of things going on here, but I think that it's important for TA teams to realize that this is the potential direction that we're going in and they need to be ready for that.

30:06Matt Alder: They do. I'm old enough to remember being allowed to take a calculator into maths tests and exams at school. And it's a tool. So bringing the tool and embracing the fact that the tool can be used in the work and you want to hire the people that are using the tool in the right way, that's key. So bringing the tool into the process, the tool in general, AI, and a multitude of different agents. The critical piece is, I think, that TA teams need to separate out what the base criteria of the human experience is. Going back to earlier in the conversation, the experience that's been built through collaboration and through the journey, through the career and so on, and isolating that piece out and then being very, very transparent as to what they require the human to provide and then what they will allow the AI to be part of.

31:03Matt Alder: And we've tried to play with that a little bit with application forms recently using subtext and nested questions and a whole bunch of other things to really showcase the humans that are thinking very deliberately about their application and the experience they've had and those that have just deployed agents to throw as much mud at the wall as possible so that we can avoid having to spend a lot of human time on the needle in the needle, the haystack full of needles.

31:30Jim Miller: Absolutely. So we are kind of pretty much out of time because we've sort of gone through the all in a lot of detail. I want to just finish though, because you've sort of mentioned this a few times as we've sort of gone through the conversation about your own recruiting process, because you mentioned you sort of copied bits off the civil set. Tell us how it works and why it's different.

31:51Matt Alder: So we have evergreen roles. So the roles that we're constantly hiring, and that's all down to a calculation of time to fill and how quickly do we need the next one. So that sits to one side. So every single one-off role, we will post the job with a job description that's a narrative style that gives an enormous amount of information, including why you should not apply for the job, deep, deep transparency. And I've got data around how that transparency impacts things downstream. Every job has a closing date and we give the instructions to the candidates to take their time over the application because the application will be the tightbreaker, not the CV.

32:29Matt Alder: and then letting them know that we will review 100 % of the applications after the closing date. No need to message anybody, nothing at all. Everybody gets a fair crack at the whip. And there's a statistical model behind that, because if you only review the first 10 % of the applicant pool, the likelihood that the best candidate, as subjective as that statement is, is in the first 10 % is very unlikely. Likewise, the best candidate from any particular demographic however you want to slice and dice demographics is unlikely to be in the first 10%. So we're capturing the entire pool here. We source before the job goes live.

33:06Matt Alder: So we have a pool of candidates that we like the look of from sourcing. They get the outreach on the day the job goes live. So they see that the outreach has happened on the day and we call out the closing date and we're asking them to apply. So to the point around the plethora or the huge amount of information, the spam going out and all the noise. Hopefully this cuts through that. Referrals, internal candidates all go into the same pool. Then we stop and we have the time set aside to review that entire pool we've created. We use data to inform how long the job is posted, by the way. That time is set aside beforehand.

33:43Matt Alder: We use the technology to go through. First pass is all the people who aren't qualified. Second pass is long list to short list, still objectively. Then the hiring manager's involved and they have a number they're allowed to move n forward so they might have three x in the pool they've now got the difficult job of choosing who goes forward to that next round and the moment they do we release the time that's been held for the interviews everyone gets the interview scheduling automation at exactly the same moment and they have free open calendar space to fill the calendar so now that cohort has been built and they will move through the process at roughly the same pace because they're all interviewing within 24 hours of the release of the interview times.

34:28Matt Alder: What that enables this is, first of all, the hiring managers never come back and say, I'd like to see a few more. It gives us choice at the end of the process, keeps everybody together. I think it's much more objective and fair. And we're never stack ranking the candidates. Everybody who, you get past each stage, you move through the process. the only time you would make a choice is after the hiring bar has been met and we've had our debrief and we decided we've got three candidates who are above the bar which one does the hiring manager and the departmental head want to hire from that group makes sense fantastic jim thank

35:04Jim Miller: you so much for for joining me it's been brilliant talking brilliant talking to you um thanks to everyone who is watching this or if you're listening to it uh on the podcast um lots of great content coming up on Recruiting Future in the next few weeks. Really, really big topics. So if you're not following us, wherever you listen to your podcast, please make sure that you do. And finally, Roundup Live will be back again this time next month. So thanks very much and speak to you all soon.

35:49Thank you.